Abstract
Detecting faces in images is a key step in numerous computer vision applications, such as face recognition or facial expression analysis. Automatic face detection is a difficult task because of the large face intra-class variability which is due to the important influence of the environmental conditions on the face appearance. We propose a cascade face detection method based on histograms of oriented gradients (HOG), using different kinds of features and classifier to exclude non-face step by step. The candidate feature set was constructed by HOG feature of different grain size, at different stages the support vector machine (SVM) was used as the weak classifier with different parameters. Experimental results showed a better performance compared to the state-of-the-art on CMU/MIT datasets.
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